Signal monitoring continues to be a field of great importance in order to provide improved responsiveness in a variety of time critical contexts. For example, early warnings of events which may cause natural disasters can provide essential time for evacuation or emergency preparedness. It is also desirable to detect the presence of human, animal or equipment activity with a low error rate in order to counter threatening activities including military operations, border intrusions and trafficking of illegal goods.
In the past it has been commonplace to employ multi-modal sensing schemes to characterize such activity in an automated or quasi-automated manner. For example, it is conventional to employ a combination of sensor systems to discriminate certain sources from others. In one implementation there may be acquisition of temperature, infrared data, magnetic sensing which, in combination, can be used to confirm the presence of a specific object such as a type of air craft or terrestrial vehicle. Such systems are complex and often not portable due to size and weight. They are not well-suited for rapid deployment and, generally, consume levels of power that make long term battery powered operation impractical. Such objects of interest have also been identified on the basis of data matching wherein the source, e.g., a moving motor vehicle, is known to have a generic signature. Acquisition of time varying power density and spectral data associated with specific sources of seismic or acoustic energy can be compared with a fingerprint template for a specific vehicle type to determine whether the vehicle is a motor cycle or a truck. Due to the varied nature of signatures within a category (e.g., moving trucks), such fingerprint matching techniques may have an unacceptably high rate of false detections or may result in error, i.e., a failure to identify a vehicle as being in a suspect class. There is a need to provide systems and methods which enable rapid detection of specific types of sources with high levels of confidence.